Exploration of the shared diagnostic genes and molecular mechanism between obesity and atherosclerosis via bioinformatic analysis.

Obesity (OB) and atherosclerosis (AS) represent two highly prevalent and detrimental chronic diseases that are intricately linked. However, the shared genetic signatures and molecular pathways underlying these two conditions remain elusive. This study aimed to identify the shared diagnostic genes and the associated molecular mechanism between OB and AS. The microarray datasets of OB and AS were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) analysis and the weighted gene co-expression network analysis (WGCNA) were conducted to identify the shared genes. Then least absolute shrinkage selection (LASSO) algorithm was used for diagnostic genes discovery. The diagnostic genes were validated using expression analysis and receiver operating characteristic (ROC) curves. Furthermore, Gene set enrichment analysis (GSEA) was used to investigate molecular pathways and immune infiltration related to the diagnostic genes. TF-gene and miRNA-gene networks were also constructed by utilizing the NetworkAnalyst tool. By intersecting the key module genes of WGCNA with DEGs in OB and AS, 56 shared genes with the same expression trend were identified. Using LASSO algorithm, we obtained two shared diagnostic genes, namely SAMSN1 and PHGDH. Validation confirmed their expression patterns and robust predictive abilities. GSEA revealed the crucial roles of SAMSN1 and PHGDH in disease-associated pathways. Additionally, higher immune cell infiltration expression was found in both diseases and strongly linked to the diagnostic genes. Finally, we constructed the TF-gene and miRNA-gene networks. We identified SAMSN1 and PHGDH as potential diagnostic genes for OB and AS. Our findings provide novel insights into the molecular underpinnings of the OB-AS link.